The present invention relates to the publication of media assets and, in particular, techniques for prioritizing media assets for publication.
Publication of media assets occurs in many different contexts and involves different types of content. In most contexts there are constraints on how many assets can be presented or displayed to the user, and on how those assets are displayed (e.g., location on a page, time of day, etc.).
In the case of a daily newspaper, the available media assets might be a set of articles. To publish a particular day's edition, a subset of available articles are chosen with certain articles being excluded due to space constraints, cost, editorial policies, etc. Articles intended to receive the most emphasis are typically placed in prominent positions on the front page of the newspaper.
In the case of a television station that sets its programming lineup on a weekly basis, the available media assets might be a set of shows, series, and movies. To create a particular week's schedule, a subset of these shows are selected and mapped to a 7 day by 24 hour grid.
In the case of an Internet media property, the assets may be URLs and article summaries. Articles intended to receive the most emphasis often appear at the top of the Internet media property home page. Because an Internet media property may be changed at arbitrary time intervals, media publishing can be considered an almost continuous process in which changes in article selection and article emphases reflect changing prioritization of media assets.
Conventionally, media publishing has required direct human involvement in the content programming process. Typically, an editor with an understanding of the target audience (e.g., readers for newspapers and magazines, viewers for television, listeners for radio, etc.) would select content for publication and determine how those assets were to be displayed. Fundamental trends in media creation and consumption are marginalizing the role of the human editor in many of these applications.
For one thing, the number of assets available for programming has increased (and continues to increase) exponentially. Increased digitization of media, improved tools for content licensing, new standards like RSS for content sharing, and the rise of user generated content (blogs, micropublishing, Flickr, YouTube) all contribute to these increases. It is now impossible in many cases for a human editor or editorial staff to even be aware of all of the relevant media assets, much less prioritize them for publication.
In addition, the expectation of users for fresh, personally relevant content has also increased. Fragmentation of media sources combined with easy access to those media sources through the Internet and various search technologies give users unprecedented control over the types of content they consume. Ubiquitous connectivity to news websites, real-time communication channels, television, and radio has reduced information cycle times from hours to seconds. These phenomena pose further challenges for publishing models based on human editorial resource in that satisfying these expectations effectively requires instantaneous knowledge of audience interests, and the ability to customize content delivered to various segments of that audience, all the way down to the individual user. While it may be possible to publish media at close to real time (5-10 minute increments) using a human editor approach for very large audiences (e.g., viewers of the Yahoo! homepage, CNN, etc.), it is not feasible to do so for the smaller audiences associated with high personal or regional relevance.
One set of solutions to this media publishing problem can be referred to as “interactive media.” In interactive media, the work of the human editor is effectively distributed to the end consumer. In its most extreme form, the available media assets are stored in a search index and it is up to the user to decide the content they are interested in, type in a search query, and select from the search results. Other interactive media solutions provide the end consumer with the ability to “subscribe” to media assets from selected sources (e.g., RSS feeds, “My sources,” etc.), or provide a list of keywords of interest (e.g., for news alerts, etc.). These solutions act as filters on the underlying sets of media assets.
Another set of solutions can be characterized as editor productivity tools. These are technology systems and publishing models that seek to reduce the work needed for editors to prioritize content. Examples include categorization systems that provide structured views of available media assets, cluster related media assets, provide alerts on breaking events, etc. Other examples include the use of aggregation systems to re-syndicate media publishing decisions made by others such as, for example, media publishers with a particular expertise, e.g., a geographical market expertise for a region, city, town neighborhood, etc., or an expertise for particular subject matter, e.g., sports, entertainment, finance, etc.
One approach employed by a major online news site looks at the aggregation of articles from many sources to determine a prioritization of stories based on factors like how many articles are being written on a particular topic. This can be considered an example of an editor productivity tool (specifically, aggregation) taken to the extreme of removing the editor all together.
Another approach employed by a well known aggregator site determines a prioritization of web pages/articles based on the number of users who save or bookmark that web page or article. This is a variant of the “interactive media” solution which engages users outside of the website site for the purpose of helping to prioritize and select content on the website.
Some websites have implemented systems that prioritize media assets by allowing users to rate stories, or vote stories “up” or “down.” These and similar approaches may also be characterized as “interactive media” approaches in which the action of one or more users can potentially change the media published (prioritization and/or selection) for users other than those who rated or voted on the stories. In addition to explicit rating or voting, some websites achieve similar results with implicit rating or voting, e.g., the reordering of articles based on click-through-rate, changing television programming based on viewer numbers, or providing lists of “most popular” or “most emailed” content.
Unfortunately, these approaches still fall short of being able to present relevant content to users on a scale which corresponds to the rate at which content is now produced.